A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

Hari, Vittal; Singh, Jitendra; Kumar, Pankaj; Karmakar, Subhankar

doi:10.1016/j.jhydrol.2015.04.024

Cited by 45 publications

(29 citation statements)

References 98 publications

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“…the number of peaks, and the variance in the estimated shape and scale parameters was investigated. For the two parameters, the variance was found to increase with a decrease in the sample size, a result that agrees with those of other studies [13,25]. See supplemental Table S2 for the estimated parameters and their variance, and Figure S1 and Figure S2 for the relationship between the variance in shape and scale and the sample size.…”

Section: Application Of Pot To Observed Streamflowssupporting

confidence: 88%

Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis

Gharib

Davies

Goss

et al. 2017

Water

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Abstract:Floods are costly natural disasters that are projected to increase in severity and frequency into the future. Exceedances over a high threshold and analysis of their distributions, as determined through the Peak Over Threshold (POT) method and approximated by a Generalized Pareto Distribution (GPD), respectively, are widely used for flood frequency analysis. This study investigates the combined effects of threshold selection and GPD parameter estimation on the accuracy of flood quantile estimates, and develops a new, widely-applicable framework that significantly improves the accuracy of flood quantile estimations. First, the performance of several parameter estimators (i.e., Maximum Likelihood; Probability Weighted Moments; Maximum Goodness of Fit; Likelihood Moment; Modified Likelihood Moment; and Nonlinear Weighted Least Square Error) for the GPD was compared through Monte Carlo simulation. Then, a calibrated Soil and Water Assessment Tool (SWAT) model for the province of Alberta, Canada, was used to reproduce daily streamflow series for 47 watersheds distributed across the province, and the POT was applied to each. The Goodness of Fit for the resulting flood frequency models was measured by the upper tail Anderson-Darling (AD) test and the root-mean-square error (RMSE) and demonstrated improvements for more than one-third of stations by averages of 65% (AD) and 47% (RMSE), respectively.

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Section: Application Of Pot To Observed Streamflowssupporting

confidence: 88%

Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis

Gharib

Davies

Goss

et al. 2017

Water

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“…A kernel distribution is a nonparametric representation of the pdf of a random variable. The kernel density estimator is the fundamental data smoothing technique . The kernel density estimator is the estimated pdf of the random variable.…”

Section: Methodsmentioning

confidence: 99%

“…However, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, and summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set …”

Section: Methodsmentioning

confidence: 99%

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A Framework for Assessing Uncertainty Associated with Human Health Risks from MSW Landfill Leachate Contamination

et al. 2016

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Landfilling is a cost-effective method, which makes it a widely used practice around the world, especially in developing countries. However, because of the improper management of landfills, high leachate leakage can have adverse impacts on soils, plants, groundwater, aquatic organisms, and, subsequently, human health. A comprehensive survey of the literature finds that the probabilistic quantification of uncertainty based on estimations of the human health risks due to landfill leachate contamination has rarely been reported. Hence, in the present study, the uncertainty about the human health risks from municipal solid waste landfill leachate contamination to children and adults was quantified to investigate its long-term risks by using a Monte Carlo simulation framework for selected heavy metals. The Turbhe sanitary landfill of Navi Mumbai, India, which was commissioned in the recent past, was selected to understand the fate and transport of heavy metals in leachate. A large residential area is located near the site, which makes the risk assessment problem both crucial and challenging. In this article, an integral approach in the form of a framework has been proposed to quantify the uncertainty that is intrinsic to human health risk estimation. A set of nonparametric cubic splines was fitted to identify the nonlinear seasonal trend in leachate quality parameters. LandSim 2.5, a landfill simulator, was used to simulate the landfill activities for various time slices, and further uncertainty in noncarcinogenic human health risk was estimated using a Monte Carlo simulation followed by univariate and multivariate sensitivity analyses.

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“…Salvadori and De Michele, 2004;Favre et al, 2004;Grimaldi and Serinaldi, 2006;Vittal et al, 2015). Traditional statistical analysis techniques tend to focus on either left or right tail properties of statistical distributions, but not necessarily using the same statistical framework.…”

Section: Statistical Analysis Of Hydrological Responsementioning

confidence: 99%

Statistical analysis of hydrological response in urbanising catchments based on adaptive sampling using inter-amount times

Veldhuis

Schleiss

2017

Hydrol. Earth Syst. Sci.

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Abstract. Urban catchments are typically characterised by a more flashy nature of the hydrological response compared to natural catchments. Predicting flow changes associated with urbanisation is not straightforward, as they are influenced by interactions between impervious cover, basin size, drainage connectivity and stormwater management infrastructure. In this study, we present an alternative approach to statistical analysis of hydrological response variability and basin flashiness, based on the distribution of inter-amount times. We analyse inter-amount time distributions of high-resolution streamflow time series for 17 (semi-)urbanised basins in North Carolina, USA, ranging from 13 to 238 km 2 in size. We show that in the inter-amount-time framework, sampling frequency is tuned to the local variability of the flow pattern, resulting in a different representation and weighting of high and low flow periods in the statistical distribution. This leads to important differences in the way the distribution quantiles, mean, coefficient of variation and skewness vary across scales and results in lower mean intermittency and improved scaling. Moreover, we show that inter-amount-time distributions can be used to detect regulation effects on flow patterns, identify critical sampling scales and characterise flashiness of hydrological response. The possibility to use both the classical approach and the inter-amount-time framework to identify minimum observable scales and analyse flow data opens up interesting areas for future research.

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A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

Cited by 45 publications

References 98 publications

Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis

Assessment of the Combined Effects of Threshold Selection and Parameter Estimation of Generalized Pareto Distribution with Applications to Flood Frequency Analysis

A Framework for Assessing Uncertainty Associated with Human Health Risks from MSW Landfill Leachate Contamination

Statistical analysis of hydrological response in urbanising catchments based on adaptive sampling using inter-amount times

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